Chapter 13 Source of functional differences
# A tibble: 3 × 2
type_river count
<chr> <int>
1 core 25
2 endemic 7
3 marginal 11***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************13.1 Taxonomic variation
genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
left_join(., sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
filter(count > 0) %>% #filter 0 counts
left_join(core_microbiota,by="genome") %>%
group_by(type,sample) %>%
summarise(fraction=sum(count)) %>%
group_by(type) %>%
summarise(mean(fraction))`summarise()` has grouped output by 'type'. You can override using the `.groups` argument.# A tibble: 3 × 2
type `mean(fraction)`
<chr> <dbl>
1 core 0.901
2 endemic 0.132
3 marginal 0.087413.2 Genome size
# A tibble: 3 × 2
type genome_size
<chr> <dbl>
1 core 3337341.
2 endemic 3753149.
3 marginal 3281875.# A tibble: 3 × 9
.y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 length core endemic 25 7 69 0.42 0.973 ns
2 length core marginal 25 11 139 0.973 0.973 ns
3 length endemic marginal 7 11 44 0.659 0.973 ns 
## Functional differences between fractions
genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
to.domains(., GIFT_db) %>%
as.data.frame() %>%
mutate(mci=(Biosynthesis+Degradation)/2) %>%
rownames_to_column(var="genome") %>%
select(genome,mci) %>%
left_join(core_microbiota %>% select(genome,type),by="genome") %>%
pivot_longer(!c(genome,type),names_to = "trait",values_to = "value") %>%
filter(!is.na(type)) %>%
ggplot(aes(x=type, y=value, group=type))+
geom_boxplot()+
facet_grid(~trait, scales="free")
genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
to.domains(., GIFT_db) %>%
as.data.frame() %>%
mutate(mci=(Biosynthesis+Degradation)/2) %>%
rownames_to_column(var="genome") %>%
select(genome,mci) %>%
left_join(core_microbiota %>% select(genome,type),by="genome") %>%
pivot_longer(!c(genome,type),names_to = "trait",values_to = "value") %>%
filter(!is.na(type)) %>%
group_by(trait) %>%
pairwise_wilcox_test(value ~ type, p.adjust.method = "BH")# A tibble: 3 × 10
trait .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 mci value core endemic 23 7 72 0.701 0.701 ns
2 mci value core marginal 23 10 177 0.014 0.042 *
3 mci value endemic marginal 7 10 53 0.088 0.132 ns genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="genome") %>%
left_join(core_microbiota %>% select(genome,type),by="genome") %>%
pivot_longer(!c(genome,type),names_to = "trait",values_to = "value") %>%
filter(!is.na(type),
trait %in% c("B01","B02","B03","B04","B06","B07","B08","D01","D02","D03","D05","D06","D07","D09")) %>%
group_by(trait) %>%
pairwise_wilcox_test(value ~ type, p.adjust.method = "BH") %>%
print(n=100)# A tibble: 42 × 10
trait .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 B01 value core endemic 23 7 57.5 0.27 0.27 ns
2 B01 value core marginal 23 10 168. 0.038 0.057 ns
3 B01 value endemic marginal 7 10 61 0.01 0.029 *
4 B02 value core endemic 23 7 69 0.598 0.598 ns
5 B02 value core marginal 23 10 173 0.024 0.073 ns
6 B02 value endemic marginal 7 10 53 0.088 0.131 ns
7 B03 value core endemic 23 7 88 0.73 0.73 ns
8 B03 value core marginal 23 10 160. 0.08 0.24 ns
9 B03 value endemic marginal 7 10 44 0.399 0.599 ns
10 B04 value core endemic 23 7 92.5 0.572 0.623 ns
11 B04 value core marginal 23 10 150. 0.182 0.546 ns
12 B04 value endemic marginal 7 10 40.5 0.623 0.623 ns
13 B06 value core endemic 23 7 79 0.961 0.961 ns
14 B06 value core marginal 23 10 153 0.142 0.256 ns
15 B06 value endemic marginal 7 10 49.5 0.171 0.256 ns
16 B07 value core endemic 23 7 65 0.471 0.471 ns
17 B07 value core marginal 23 10 174 0.022 0.042 *
18 B07 value endemic marginal 7 10 58 0.028 0.042 *
19 B08 value core endemic 23 7 54 0.202 0.202 ns
20 B08 value core marginal 23 10 171 0.029 0.044 *
21 B08 value endemic marginal 7 10 62 0.01 0.029 *
22 D01 value core endemic 23 7 98.5 0.39 0.585 ns
23 D01 value core marginal 23 10 172. 0.028 0.084 ns
24 D01 value endemic marginal 7 10 39 0.73 0.73 ns
25 D02 value core endemic 23 7 66.5 0.508 0.508 ns
26 D02 value core marginal 23 10 168. 0.038 0.113 ns
27 D02 value endemic marginal 7 10 53 0.086 0.13 ns
28 D03 value core endemic 23 7 84 0.883 0.883 ns
29 D03 value core marginal 23 10 182. 0.01 0.029 *
30 D03 value endemic marginal 7 10 56.5 0.038 0.057 ns
31 D05 value core endemic 23 7 90 0.666 0.666 ns
32 D05 value core marginal 23 10 169 0.034 0.104 ns
33 D05 value endemic marginal 7 10 46 0.315 0.473 ns
34 D06 value core endemic 23 7 64.5 0.447 0.447 ns
35 D06 value core marginal 23 10 163 0.062 0.092 ns
36 D06 value endemic marginal 7 10 57 0.034 0.092 ns
37 D07 value core endemic 23 7 80.5 1 1 ns
38 D07 value core marginal 23 10 180. 0.012 0.036 *
39 D07 value endemic marginal 7 10 51.5 0.11 0.165 ns
40 D09 value core endemic 23 7 93 0.537 0.806 ns
41 D09 value core marginal 23 10 136. 0.387 0.806 ns
42 D09 value endemic marginal 7 10 35 1 1 ns genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="genome") %>%
left_join(core_microbiota %>% select(genome,type),by="genome") %>%
pivot_longer(!c(genome,type),names_to = "trait",values_to = "value") %>%
filter(!is.na(type)) %>%
ggplot(aes(x=type, y=value, group=type))+
geom_boxplot()+
facet_grid(~trait, scales="free")
13.3 Functional differences between high and low endemisms
genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
to.domains(., GIFT_db) %>%
as.data.frame() %>%
mutate(mci=(Biosynthesis+Degradation)/2) %>%
rownames_to_column(var="genome") %>%
select(genome,mci) %>%
left_join(core_microbiota %>% select(genome,type_prevalence_environment, type),by="genome") %>%
filter(type == "endemic") %>%
select(-type) %>%
pivot_longer(!c(genome,type_prevalence_environment),names_to = "trait",values_to = "value") %>%
filter(!is.na(type_prevalence_environment)) %>%
ggplot(aes(x=type_prevalence_environment, y=value, group=type_prevalence_environment))+
geom_boxplot()+
facet_grid(~trait, scales="free")
genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
to.domains(., GIFT_db) %>%
as.data.frame() %>%
mutate(mci=(Biosynthesis+Degradation)/2) %>%
rownames_to_column(var="genome") %>%
select(genome,mci) %>%
left_join(core_microbiota %>% select(genome,type_prevalence_environment, type),by="genome") %>%
filter(type == "endemic") %>%
select(-type) %>%
pivot_longer(!c(genome,type_prevalence_environment),names_to = "trait",values_to = "value") %>%
filter(!is.na(type_prevalence_environment)) %>%
group_by(trait) %>%
pairwise_wilcox_test(value ~ type_prevalence_environment, p.adjust.method = "BH")# A tibble: 1 × 10
trait .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 mci value high low 5 2 2 0.381 0.381 ns genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="genome") %>%
left_join(core_microbiota %>% select(genome,type_prevalence_environment, type),by="genome") %>%
filter(type == "endemic") %>%
select(-type) %>%
pivot_longer(!c(genome,type_prevalence_environment),names_to = "trait",values_to = "value") %>%
filter(!is.na(type_prevalence_environment),
trait %in% c("B01","B02","B03","B04","B06","B07","B08","D01","D02","D03","D05","D06","D07","D09")) %>%
ggplot(aes(x=type_prevalence_environment, y=value, group=type_prevalence_environment))+
geom_boxplot()+
facet_grid(~trait, scales="free")
genome_gifts %>%
to.elements(., GIFT_db) %>%
to.functions(., GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="genome") %>%
left_join(core_microbiota %>% select(genome,type_prevalence_environment, type),by="genome") %>%
filter(type == "endemic") %>%
select(-type) %>%
pivot_longer(!c(genome,type_prevalence_environment),names_to = "trait",values_to = "value") %>%
filter(!is.na(type_prevalence_environment),
trait %in% c("B01","B02","B03","B04","B06","B07","B08","D01","D02","D03","D05","D06","D07","D09")) %>%
group_by(trait) %>%
pairwise_wilcox_test(value ~ type_prevalence_environment, p.adjust.method = "BH") %>%
print(n=100)# A tibble: 14 × 10
trait .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
* <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
1 B01 value high low 5 2 2 0.381 0.381 ns
2 B02 value high low 5 2 2 0.381 0.381 ns
3 B03 value high low 5 2 5 1 1 ns
4 B04 value high low 5 2 4 0.857 0.857 ns
5 B06 value high low 5 2 1.5 0.241 0.241 ns
6 B07 value high low 5 2 1 0.19 0.19 ns
7 B08 value high low 5 2 2 0.329 0.329 ns
8 D01 value high low 5 2 1 0.171 0.171 ns
9 D02 value high low 5 2 0 0.079 0.079 ns
10 D03 value high low 5 2 0 0.079 0.079 ns
11 D05 value high low 5 2 1 0.19 0.19 ns
12 D06 value high low 5 2 2 0.381 0.381 ns
13 D07 value high low 5 2 2 0.381 0.381 ns
14 D09 value high low 5 2 2 0.285 0.285 ns 13.4 MAGs in different locations and shared among locations
13.4.1 Core
genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
left_join(., sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
filter(count > 0) %>% #filter 0 counts
left_join(core_microbiota,by="genome") %>%
filter(type=="core") %>%
ggplot(., aes(x=sample,y=count, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
scale_fill_manual(values=phylum_colors) +
facet_nested(~factor(environment, labels=c("low" = "Low altitude", "high" = "High altitude")), scales="free") + #facet per day and treatment
guides(fill = guide_legend(ncol = 1)) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = "white"),
strip.text = element_text(size = 12, lineheight = 0.6,face="bold"),
axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black")) +
labs(fill="Phylum",y = "Relative abundance",x="Samples")
13.4.2 Endemic
genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
left_join(., sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
filter(count > 0) %>% #filter 0 counts
left_join(core_microbiota,by="genome") %>%
filter(type=="endemic") %>%
ggplot(., aes(x=sample,y=count, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
scale_fill_manual(values=phylum_colors) +
ylim(0,1)+
facet_nested(~factor(environment, labels=c("low" = "Low altitude", "high" = "High altitude")), scales="free") + #facet per day and treatment
guides(fill = guide_legend(ncol = 1)) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = "white"),
strip.text = element_text(size = 12, lineheight = 0.6,face="bold"),
axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black")) +
labs(fill="Phylum",y = "Relative abundance",x="Samples")
13.4.3 Marginal
genome_counts_filt %>%
mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
left_join(., sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
filter(count > 0) %>% #filter 0 counts
left_join(core_microbiota,by="genome") %>%
filter(type=="marginal") %>%
ggplot(., aes(x=sample,y=count, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
ylim(0,1)+
scale_fill_manual(values=phylum_colors) +
facet_nested(~factor(environment, labels=c("low" = "Low altitude", "high" = "High altitude")), scales="free") + #facet per day and treatment
guides(fill = guide_legend(ncol = 1)) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = "white"),
strip.text = element_text(size = 12, lineheight = 0.6,face="bold"),
axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black")) +
labs(fill="Phylum",y = "Relative abundance",x="Samples")
13.4.4 Ordination
gift_pcoa <- genome_gifts %>%
to.elements(., GIFT_db) %>%
as.data.frame() %>%
vegdist(method="euclidean") %>%
pcoa()
gift_pcoa_rel_eigen <- gift_pcoa$values$Relative_eig[1:10]
# Get genome positions
gift_pcoa_vectors <- gift_pcoa$vectors %>% #extract vectors
as.data.frame() %>%
select(Axis.1,Axis.2) # keep the first 2 axes
gift_pcoa_eigenvalues <- gift_pcoa$values$Eigenvalues[c(1,2)]
gift_pcoa_gifts <- cov(genome_gifts, scale(gift_pcoa_vectors)) %*% diag((gift_pcoa_eigenvalues/(nrow(genome_gifts)-1))^(-0.5)) %>%
as.data.frame() %>%
rename(Axis.1=1,Axis.2=2) %>%
rownames_to_column(var="label") %>%
#get function summary vectors
mutate(func=substr(label,1,3)) %>%
group_by(func) %>%
summarise(Axis.1=mean(Axis.1),
Axis.2=mean(Axis.2)) %>%
rename(label=func) %>%
filter(!label %in% c("S01","S02","S03"))set.seed(101)
scale <- 20 # scale for vector loadings
gift_pcoa_vectors %>%
rownames_to_column(var="genome") %>%
left_join(genome_metadata, by="genome") %>%
ggplot() +
#genome positions
scale_color_manual(values=phylum_colors)+
geom_point(aes(x=Axis.1,y=Axis.2, color=phylum, size=length),
alpha=0.9, shape=16) +
#scale_color_manual(values=phylum_colors) +
scale_size_continuous(range = c(0.1,5)) +
#loading positions
geom_segment(data=gift_pcoa_gifts,
aes(x=0, y=0, xend=Axis.1 * scale, yend=Axis.2 * scale),
arrow = arrow(length = unit(0.3, "cm"),
type = "open",
angle = 25),
linewidth = 0.5,
color = "black") +
#Primary and secondary scale adjustments
scale_x_continuous(name = paste0("PCoA1 (",round(gift_pcoa_rel_eigen[1]*100, digits = 2), " %)"),
sec.axis = sec_axis(~ . / scale, name = "Loadings on PCoA1")
) +
scale_y_continuous(name = paste0("PCoA2 (",round(gift_pcoa_rel_eigen[2]*100, digits = 2), " %)"),
sec.axis = sec_axis(~ . / scale, name = "Loadings on PCoA2")
) +
geom_label_repel(data = gift_pcoa_gifts,
aes(label = label, x = Axis.1 * scale, y = Axis.2 * scale),
segment.color = 'transparent') +
xlim(-4.5,4.5) +
ylim(-3,2.5) +
theme_minimal() +
theme(legend.position = "none")
structural <- core_microbiota %>%
mutate(type = case_when(
(type_prevalence == "endemic") & (type_prevalence_environment == "high") ~ "present_high",
(type_prevalence == "endemic") & (type_prevalence_environment == "low") ~ "present_low")) %>%
filter(!is.na(type)) %>%
select(genome,type)
enriched <- ancom_mag_skin_table %>%
mutate(type = case_when(
lfc_environmentlow < 0 ~ "enriched_low",
lfc_environmentlow >= 0 ~ "enriched_high")) %>%
select(genome,type)
gift_pcoa_vectors %>%
rownames_to_column(var="genome") %>%
left_join(bind_rows(structural,enriched), by="genome") %>%
left_join(genome_metadata, by="genome") %>%
mutate(type=ifelse(is.na(type),"neutral",type)) %>%
group_by(type) %>%
mutate(x_cen = median(Axis.1, na.rm = TRUE)) %>%
mutate(y_cen = median(Axis.2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot() +
scale_size_continuous(range = c(0.1,5)) +
geom_point(aes(x=Axis.1,y=Axis.2, color=type, size=length), alpha=0.9, shape=16) +
geom_segment(aes(x = x_cen, y = y_cen, xend = Axis.1, yend = Axis.2, color=type), alpha = 0.5) +
scale_color_manual(values=c("#5A99D2","#F16144","#B7B7B7","#225072","#8C2C1C"))+
theme_minimal()
scale <- 15 # scale for vector loadings
gift_pcoa_vectors %>%
rownames_to_column(var="genome") %>%
left_join(core_microbiota, by="genome") %>%
left_join(genome_metadata, by="genome") %>%
filter(type != "absent") %>%
mutate(shape = case_when(
type == "core" ~ "core",
type == "marginal" ~ "marginal",
type == "endemic" & type_prevalence_environment == "high" ~ "endemic_high",
type == "endemic" & type_prevalence_environment == "low" ~ "endemic_low"
)) %>%
group_by(shape) %>%
mutate(x_cen = median(Axis.1, na.rm = TRUE)) %>%
mutate(y_cen = median(Axis.2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot() +
#genome positions
geom_segment(aes(x = x_cen, y = y_cen, xend = Axis.1, yend = Axis.2, color=shape), alpha = 0.5, show.legend = FALSE) +
scale_color_manual(values=c("#9E8F71","#5A99D2","#F16144","#B7B7B7"))+
theme_minimal() 
# A tibble: 33 × 5
gift high low p_value p_adjust
<chr> <dbl> <dbl> <dbl> <dbl>
1 B0104 0.681 0.824 0.00578 0.0279
2 B0106 0.894 0.747 0.00228 0.0123
3 B0208 0.476 0.720 0.000794 0.00631
4 B0213 0.582 0.795 0.00000629 0.000788
5 B0220 0.179 0.311 0.00770 0.0359
6 B0302 0.233 0.457 0.00193 0.0108
7 B0307 0.370 0.201 0.000193 0.00260
8 B0309 0.134 0.269 0.000956 0.00679
9 B0402 0.222 0.357 0.000658 0.00631
10 B0403 0.264 0.398 0.0116 0.0474
# ℹ 23 more rows
| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| environment | 1 | 14.464879 | 0.1785608 | 6.438403 | 0.001 |
| river | 2 | 8.130211 | 0.1003629 | 1.809402 | 0.097 |
| Residual | 26 | 58.413069 | 0.7210764 | NA | NA |
| Total | 29 | 81.008159 | 1.0000000 | NA | NA |
| gift | high | low | p_value | p_adjust |
|---|---|---|---|---|
| B0104 | 0.67421663 | 1.0000000 | 0.0018754356 | 0.005672049 |
| B0105 | 0.73759193 | 1.0000000 | 0.0018754356 | 0.005672049 |
| B0106 | 0.80621259 | 1.0000000 | 0.0017459182 | 0.005672049 |
| B0204 | 0.48537488 | 0.8606981 | 0.0145424059 | 0.028623148 |
| B0206 | 0.61242517 | 1.0000000 | 0.0017937971 | 0.005672049 |
| B0207 | 0.43143388 | 0.7100000 | 0.0019749754 | 0.005695278 |
| B0209 | 0.79386259 | 1.0000000 | 0.0018754356 | 0.005672049 |
| B0210 | 0.55247656 | 0.8606981 | 0.0145424059 | 0.028623148 |
| B0213 | 0.61084133 | 0.8944683 | 0.0145424059 | 0.028623148 |
| B0214 | 0.22485035 | 0.6834048 | 0.0219467710 | 0.041233327 |
| B0218 | 0.46423032 | 0.7889365 | 0.0256749426 | 0.046819013 |
| B0220 | 0.07385765 | 0.7171750 | 0.0010154015 | 0.005672049 |
| B0601 | 0.54689170 | 0.9155746 | 0.0079058708 | 0.017824145 |
| B0602 | 0.63919238 | 0.9155746 | 0.0145424059 | 0.028623148 |
| B0603 | 0.66787867 | 1.0000000 | 0.0018754356 | 0.005672049 |
| B0604 | 0.43445679 | 1.0000000 | 0.0018754356 | 0.005672049 |
| B0605 | 0.00000000 | 0.4221269 | 0.0045906627 | 0.011161611 |
| B0702 | 0.66854993 | 1.0000000 | 0.0018754356 | 0.005672049 |
| B0703 | 0.07420061 | 0.6700000 | 0.0004222482 | 0.005672049 |
| B0704 | 0.48294837 | 1.0000000 | 0.0018754356 | 0.005672049 |
| B0706 | 0.41331224 | 1.0000000 | 0.0018754356 | 0.005672049 |
| B0707 | 0.76551369 | 1.0000000 | 0.0018754356 | 0.005672049 |
| B0710 | 0.06335476 | 0.1400000 | 0.0007537527 | 0.005672049 |
| B0711 | 0.14734772 | 0.2664340 | 0.0010154015 | 0.005672049 |
| B0712 | 0.71438862 | 0.3700429 | 0.0020470591 | 0.005768985 |
| B0803 | 0.73937330 | 1.0000000 | 0.0018754356 | 0.005672049 |
| B0804 | 0.52266792 | 0.8606981 | 0.0145424059 | 0.028623148 |
| B1028 | 0.03147905 | 0.1400000 | 0.0014496951 | 0.005672049 |
| B1029 | 0.00000000 | 0.1444683 | 0.0045906627 | 0.011161611 |
| D0102 | 0.33798384 | 1.0000000 | 0.0018754356 | 0.005672049 |
| D0103 | 0.16414075 | 0.6700000 | 0.0063405256 | 0.014834437 |
| D0104 | 0.31230836 | 0.8000000 | 0.0019585157 | 0.005695278 |
| D0201 | 0.06134920 | 0.8100000 | 0.0007255498 | 0.005672049 |
| D0202 | 0.16943443 | 0.8037702 | 0.0010154015 | 0.005672049 |
| D0203 | 0.22479340 | 0.4890978 | 0.0008505648 | 0.005672049 |
| D0205 | 0.03972843 | 0.3057359 | 0.0010154015 | 0.005672049 |
| D0206 | 0.00000000 | 0.6115146 | 0.0002918961 | 0.005672049 |
| D0207 | 0.11256387 | 0.6128250 | 0.0010154015 | 0.005672049 |
| D0208 | 0.13134920 | 0.4595489 | 0.0009808556 | 0.005672049 |
| D0209 | 0.14134920 | 0.6079485 | 0.0010066900 | 0.005672049 |
| D0210 | 0.19001248 | 0.2500000 | 0.0018754356 | 0.005672049 |
| D0211 | 0.00000000 | 0.2195918 | 0.0045906627 | 0.011161611 |
| D0213 | 0.00000000 | 0.3093019 | 0.0002918961 | 0.005672049 |
| D0302 | 0.00000000 | 0.3300000 | 0.0001880890 | 0.005672049 |
| D0306 | 0.11242517 | 0.5000000 | 0.0013515402 | 0.005672049 |
| D0308 | 0.05621259 | 0.4422127 | 0.0007395593 | 0.005672049 |
| D0309 | 0.25000000 | 0.6055317 | 0.0007325316 | 0.005672049 |
| D0505 | 0.21728768 | 0.5264768 | 0.0145424059 | 0.028623148 |
| D0506 | 0.16892979 | 0.5000000 | 0.0018099404 | 0.005672049 |
| D0507 | 0.19124896 | 0.6273362 | 0.0010154015 | 0.005672049 |
| D0508 | 0.08094613 | 0.2586895 | 0.0219467710 | 0.041233327 |
| D0509 | 0.48650715 | 0.6444683 | 0.0223620729 | 0.041386523 |
| D0511 | 0.32436673 | 1.0000000 | 0.0018754356 | 0.005672049 |
| D0512 | 0.22485035 | 0.6834048 | 0.0219467710 | 0.041233327 |
| D0513 | 0.06335476 | 0.3709022 | 0.0010154015 | 0.005672049 |
| D0601 | 0.36577691 | 0.5000000 | 0.0018754356 | 0.005672049 |
| D0604 | 0.00000000 | 0.4221269 | 0.0045906627 | 0.011161611 |
| D0610 | 0.06917013 | 0.0000000 | 0.0051195000 | 0.012208038 |
| D0611 | 0.11242517 | 0.5000000 | 0.0013515402 | 0.005672049 |
| D0613 | 0.87641709 | 1.0000000 | 0.0126508551 | 0.027521158 |
| D0704 | 0.56941341 | 1.0000000 | 0.0018754356 | 0.005672049 |
| D0708 | 0.11242517 | 0.3944683 | 0.0120900251 | 0.026770770 |
| D0804 | 0.67738433 | 0.0000000 | 0.0006075647 | 0.005672049 |
| D0807 | 0.69205678 | 0.1544254 | 0.0020946080 | 0.005771809 |
| D0813 | 0.00000000 | 0.2889365 | 0.0045906627 | 0.011161611 |
| D0816 | 0.09668565 | 0.4820515 | 0.0065015311 | 0.014929442 |
| D0901 | 0.00000000 | 0.4221269 | 0.0045906627 | 0.011161611 |
| D0907 | 0.22485035 | 1.0000000 | 0.0013515402 | 0.005672049 |
| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| environment | 1 | 49.92847 | 0.4416516 | 12.05864 | 0.001 |
| river | 2 | 13.43533 | 0.1188447 | 1.62244 | 0.187 |
| Residual | 12 | 49.68566 | 0.4395037 | NA | NA |
| Total | 15 | 113.04945 | 1.0000000 | NA | NA |
# A tibble: 0 × 5
# ℹ 5 variables: gift <chr>, high <dbl>, low <dbl>, p_value <dbl>, p_adjust <dbl>
| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| environment | 1 | 12.004006 | 0.2843473 | 2.144208 | 0.106 |
| river | 1 | 7.818642 | 0.1852056 | 1.396600 | 0.318 |
| Residual | 4 | 22.393358 | 0.5304471 | NA | NA |
| Total | 6 | 42.216006 | 1.0000000 | NA | NA |
